2023
DOI: 10.3390/nano13101665
|View full text |Cite
|
Sign up to set email alerts
|

Effect of Hydrogen Annealing on Performances of BN-Based RRAM

Abstract: BN-based resistive random-access memory (RRAM) has emerged as a potential candidate for non-volatile memory (NVM) in aerospace applications, offering high thermal conductivity, excellent mechanical, and chemical stability, low power consumption, high density, and reliability. However, the presence of defects and trap states in BN-based RRAM can limit its performance and reliability in aerospace applications. As a result, higher set voltages of 1.4 and 1.23 V were obtained for non-annealed and nitrogen-annealed… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1

Citation Types

0
1
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(1 citation statement)
references
References 33 publications
0
1
0
Order By: Relevance
“…Recently, nitride-based devices have been studied to create efficient synaptic and memory devices to control accurately conductive paths and metal/semiconductor barriers in these devices [27][28][29]. The amorphous BN thin films have attracted significant attention for memory applications owing to wide-bandgap semiconductors with high-thermal conductivity and chemical stability [30,31]. The integration of BN with RRAM enables the construction of neural network models with a smaller memory footprint and capability for faster inferences [32][33][34].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, nitride-based devices have been studied to create efficient synaptic and memory devices to control accurately conductive paths and metal/semiconductor barriers in these devices [27][28][29]. The amorphous BN thin films have attracted significant attention for memory applications owing to wide-bandgap semiconductors with high-thermal conductivity and chemical stability [30,31]. The integration of BN with RRAM enables the construction of neural network models with a smaller memory footprint and capability for faster inferences [32][33][34].…”
Section: Introductionmentioning
confidence: 99%